ArticlePLoS computational biology2015
A Bayesian ensemble approach for epidemiological projections.
Article in PLoS computational biology, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed.
- Vote-processing rules for combining control recommendations from multiple models.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2022Article
- An ensemble n-sub-epidemic modeling framework for short-term forecasting epidemic trajectories: Application to the COVID-19 pandemic in the USA.PLoS computational biology · 2022Article
- An ensemblemedRxiv : the preprint server for health sciences · 2022Article
- Time series modelling methods to forecast the volume of self-assessment tax returns in the UK.Journal of applied statistics · 2022Article
- Trade-offs between individual and ensemble forecasts of an emerging infectious disease.Nature communications · 2021Article
- The Use and Misuse of Mathematical Modeling for Infectious Disease Policymaking: Lessons for the COVID-19 Pandemic.Medical decision making : an international journal of the Society for Medical Decision Making · 2021Article
- Ensemble bootstrap methodology for forecasting dynamic growth processes using differential equations: application to epidemic outbreaks.BMC medical research methodology · 2021Article
- The predicted impact of tuberculosis preventive therapy: the importance of disease progression assumptions.BMC infectious diseases · 2020Article
- Epidemiologically and Socio-economically Optimal Policies via Bayesian Optimization.Transactions of the Indian National Academy of Engineering : an international journal of engineering and technology · 2020Article
- FMD-VS: A virtual sensor to index FMD virus scattering.PloS one · 2020Article
- Article
- An ensemble approach to predicting the impact of vaccination on rotavirus disease in Niger.Vaccine · 2017Article
- Essential information: Uncertainty and optimal control of Ebola outbreaks.Proceedings of the National Academy of Sciences of the United States of America · 2017Article
- Predicting lymphatic filariasis transmission and elimination dynamics using a multi-model ensemble framework.Epidemics · 2017Article
- A comparison of two mathematical models of the impact of mass drug administration on the transmission and control of schistosomiasis.Epidemics · 2017Article
- Forecasting influenza outbreak dynamics in Melbourne from Internet search query surveillance data.Influenza and other respiratory viruses · 2016Article
- Validation of Models Used to Inform Colorectal Cancer Screening Guidelines: Accuracy and Implications.Medical decision making : an international journal of the Society for Medical Decision Making · 2016Article
- Evaluation of new scientific information onEFSA journal. European Food Safety Authority · 2016Article
- Article
- Trends in the Mechanistic and Dynamic Modeling of Infectious Diseases.Current epidemiology reports · 2016Review
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3 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Mathematical models are powerful tools for epidemiology and can be used to compare control actions. However, different models and model parameterizations may provide different prediction of outcomes. In other fields of research, ensemble modeling has been used to combine multiple projections. We explore the possibility of applying such methods to epidemiology by adapting Bayesian techniques developed for climate forecasting. We exemplify the implementation with single model ensembles based on different parameterizations of the Warwick model run for the 2001 United Kingdom foot and mouth disease outbreak and compare the efficacy of different control actions. This allows us to investigate the effect that discrepancy among projections based on different modeling assumptions has on the ensemble prediction. A sensitivity analysis showed that the choice of prior can have a pronounced effect on the posterior estimates of quantities of interest, in particular for ensembles with large discrepancy among projections. However, by using a hierarchical extension of the method we show that prior sensitivity can be circumvented. We further extend the method to include a priori beliefs about different modeling assumptions and demonstrate that the effect of this can have different consequences depending on the discrepancy among projections. We propose that the method is a promising analytical tool for ensemble modeling of disease outbreaks.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.